
!gdown "1tCyKy4p93gntCP6TtTDx4RPXrzjJvWZQ"
!gdown "19dY4DTCf1PjsCVRGJdWIk2S0ohBNHKlN"
!gdown "1-xsSNizet0ruiMgKLWRcmme8L_iDejhc"

Downloading...
From: https://drive.google.com/uc?id=1tCyKy4p93gntCP6TtTDx4RPXrzjJvWZQ
To: /content/zee-movies.dat
100% 171k/171k [00:00<00:00, 57.9MB/s]
Downloading...
From: https://drive.google.com/uc?id=19dY4DTCf1PjsCVRGJdWIk2S0ohBNHKlN
To: /content/zee-ratings.dat
100% 24.6M/24.6M [00:00<00:00, 148MB/s]
Downloading...
From: https://drive.google.com/uc?id=1-xsSNizet0ruiMgKLWRcmme8L_iDejhc
To: /content/zee-users.dat
100% 134k/134k [00:00<00:00, 52.3MB/s]


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
import re

pd.set_option('display.max_columns', None)


movies = pd.read_fwf('zee-movies.dat',encoding = 'ISO-8859-1')
ratings = pd.read_fwf('zee-ratings.dat',encoding = 'ISO-8859-1')
users = pd.read_fwf('zee-users.dat',encoding = 'ISO-8859-1')


display(movies.head(3))
display(ratings.head(3))
display(users.head(3))


movies = movies['Movie ID::Title::Genres'].str.split('::',expand=True)
movies.columns = ['movie_id','title','geners']

movies['release_year'] = movies['title'].apply(lambda x : re.findall('\([0-9]*\)',str(x)))
movies['release_year'] = movies['release_year'].apply(lambda x : re.sub('[^0-9]',"",str(x)))

display(movies[movies['release_year'].apply(lambda x : len(str(x)) >4)])

movies['release_year'] = pd.to_numeric(movies['release_year'],downcast='integer',errors='ignore')
movies['movie_id'] = movies['movie_id'].astype('int32',errors='ignore')

movies_default = movies.copy()


display(movies.head())
display(movies.describe())
display(movies.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3883 entries, 0 to 3882
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   movie_id      3883 non-null   int32  
 1   title         3883 non-null   object 
 2   geners        3858 non-null   object 
 3   release_year  3864 non-null   float64
dtypes: float64(1), int32(1), object(2)
memory usage: 106.3+ KB

None


movies.dropna(axis=0,how='any',inplace=True)


display(ratings.head())


users = users["UserID::Gender::Age::Occupation::Zip-code"].str.split('::',expand=True)
users.columns = ['user_id','gender','age', 'occupation','zip_code']

users['user_id'] = users['user_id'].astype('int32',errors='ignore')
users['age'] = users['age'].astype('int32',errors = 'ignore')
users['occupation'] = users['occupation'].astype('int32',errors = 'ignore')


users['age'].replace({
                        1: "Under 18",

                        18: "18-24",

                        25: "25-34",

                        35: "35-44",

                        45: "45-49",

                        50: "50-55",

                        56: "56+"},inplace=True)

users['occupation'].replace(
                    {
                        0: "other or not specified",

                        1: "academic/educator",

                        2: "artist",

                        3: "clerical/admin",

                        4: "college/grad student",

                        5: "customer service",

                        6: "doctor/health care",

                        7: "executive/managerial",

                        8: "farmer",

                        9: "homemaker",

                        10: "K-12 student",

                        11: "lawyer",

                        12: "programmer",

                        13: "retired",

                        14: "sales/marketing",

                        15: "scientist",

                        16: "self-employed",

                        17: "technician/engineer",

                        18: "tradesman/craftsman",

                        19: "unemployed",

                        20: "writer"
                    },inplace=True)


display(users.head())
print("\n Users table summary\n")
display(users.describe())
print("\n User table dtype\n")
display(users.info())

 Users table summary

 User table dtype

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6040 entries, 0 to 6039
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   user_id     6040 non-null   int32 
 1   gender      6040 non-null   object
 2   age         6040 non-null   object
 3   occupation  6040 non-null   object
 4   zip_code    6040 non-null   object
dtypes: int32(1), object(4)
memory usage: 212.5+ KB

None


users[users['age'] == 18]


display(ratings.head())


ratings = ratings["UserID::MovieID::Rating::Timestamp"].str.split('::',expand=True)
ratings.columns = ['user_id','movie_id','rating','timestamp']

ratings['user_id'] = ratings['user_id'].astype('int32',errors='ignore')
ratings['rating'] = ratings['rating'].astype('int32',errors = 'ignore')
ratings['movie_id'] = ratings['movie_id'].astype('int32',errors='ignore')

ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s').dt.hour
ratings.columns = ['user_id','movie_id','rating','hour']
ratings['avg_ratings'] = ratings.groupby('user_id')['rating'].transform('mean').round(2)


display(ratings.head())
display(ratings.describe())
display(ratings.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000209 entries, 0 to 1000208
Data columns (total 5 columns):
 #   Column       Non-Null Count    Dtype  
---  ------       --------------    -----  
 0   user_id      1000209 non-null  int32  
 1   movie_id     1000209 non-null  int32  
 2   rating       1000209 non-null  int32  
 3   hour         1000209 non-null  int64  
 4   avg_ratings  1000209 non-null  float64
dtypes: float64(1), int32(3), int64(1)
memory usage: 26.7 MB

None


display(users.head())
display(ratings.head())
display(movies.head())


geners = movies.copy()
geners['geners'] = geners['geners'].str.split('|')
geners = geners.explode('geners')
a = ~geners.pivot(index='movie_id',columns = 'geners',values = 'title').isna()
a = a.astype(int)
display(a.head(3))


dic = {'a':'action', 'acti':'action','adv':'adventure', 'advent':'adventure', 'adventu':'adventure', 'adventur':'adventure',
       'animati':'animation', 'chi':'children','chil':'children', 'childr':'children', 'childre':'children',
       'children':'children',"children's":'children', 'com':'comedy', 'come':'comedy', 'comed':'comedy',
       'd':'documentary', 'docu':'documentary', 'documen':'documentary', 'document':'documentary', 'documenta':'documentary',
       'dr':'drama', 'dram':'drama', 'f':'fantasy', 'fant':'fantasy', 'fantas':'fantasy', 
       'horr':'horror', 'horro':'horror', 'music':'musical', 'r':'romance', 'ro':'romance',
       'rom':'romance', 'roma':'romance', 'roman':'romance', 's':'sci-fi', 'sci':'sci-fi', 'sci-':'sci-fi', 'sci-f':'sci-fi',
       'th':'thriller', 'thri':'thriller', 'thrille':'thriller', 'wa':'western', 'war':'western', 'we':'western',
       'wester':'western',"children'":'children' }


movies_default.geners = movies_default.geners.str.lower()
temp = movies_default['geners'].str.split('|',expand=True)
temp = temp.replace(dic)
temp = temp.replace({None:""})
temp1 = temp[[0,1,2,3,4]].apply(lambda x : x[0]+'|'+x[1]+'|'+x[2]+'|'+x[3]+'|'+x[4],axis=1)
movies_default['geners'] = temp1.str.strip('|')

display(movies_default.head(5))


movies['geners'] = movies['geners'].apply(lambda x : str(x).lower())
movies['geners'] = movies['geners'].replace(dic)
display(movies.head(3))

temp = movies.copy()
temp['geners'] = temp['geners'].str.split('|')
temp = temp.explode('geners')
temp['geners'].replace(dic,inplace=True)
movies = temp
movies = movies[~movies.duplicated()]
print(f" {'*' * 50} {'Movies DataFrame'} {'*' * 50}")
display(movies.head(3))

 ************************************************** Movies DataFrame **************************************************


user_ratings = pd.merge(users, ratings, left_on = 'user_id', right_on = 'user_id', how = 'inner')

user_movie_ratings = user_ratings.merge(movies, on = 'movie_id')
user_movie_ratings = user_movie_ratings[~user_movie_ratings.duplicated()]
display(user_movie_ratings[user_movie_ratings.duplicated()])

user_item = user_movie_ratings[~user_movie_ratings[['user_id','movie_id','rating']].duplicated()]
user_item = user_item.pivot(index = 'user_id', columns = 'movie_id', values = 'rating')
user_item.fillna(0,inplace=True)
user_item_movies = users.merge(user_item.reset_index(), on = "user_id")


m = movies.pivot(index = 'movie_id',columns = 'geners',values = 'movie_id')
m = (~m.isna()).astype(int)

user_item_geners = movies[['movie_id','release_year']].merge(users.merge(ratings.merge(m.reset_index(), on = 'movie_id'), on = 'user_id'), on = 'movie_id')
user_item_geners.rename(columns = {'':'other'},inplace=True)


geners = movies.copy()
geners = ~geners.pivot(index='movie_id',columns = 'geners',values = 'movie_id').isna()
geners = geners.astype(int)
print(f" {'*' * 50} {'Geners DataFrame'} {'*' * 50}")
display(geners.head(3))

 ************************************************** Geners DataFrame **************************************************


plt.figure(figsize = (12,8))
sns.barplot(user_item_geners['occupation'].value_counts().index,user_item_geners['occupation'].value_counts(normalize=True).values )
plt.xticks(rotation = 90)
plt.show()

/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


plt.figure(figsize = (12,8))
sns.barplot(user_item_geners['gender'].value_counts().index,user_item_geners['gender'].value_counts(normalize=True).values )
plt.xticks(rotation = 90)
plt.show()

/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


occup = {
 'college/grad student':2 ,
 'other or not specified':2,
 'executive/managerial':2, 
 'academic/educator':2, 
 'technician/engineer':2,
 'programmer':1, 
 'writer':1, 
 'sales/marketing':1, 
 'artist':1, 
 'self-employed':1,
 'doctor/health care':1, 
 'clerical/admin':1, 
 'K-12 student':1, 
 'scientist':0,
 'customer service':0, 
 'lawyer':0, 
 'unemployed':0, 
 'retired':0,
 'tradesman/craftsman':0, 
 'homemaker':0, 
 'farmer':0}

age = {
     "Under 18":18,    
     "18-24":24,
     "25-34":34,
     "35-44":44,
     "45-49":49,
     "50-55":55,
     "56+":56 
     }


user_item_geners['occupation'] = user_item_geners['occupation'].map(occup)
user_item_geners['gender'] = user_item_geners['gender'].map({'M':1, 'F':0})
user_item_geners['age'] = user_item_geners['age'].map(age)

user_item_movies['occupation'] = user_item_movies['occupation'].map(occup)
user_item_movies['gender'] = user_item_movies['gender'].map({'M':1, 'F':0})
user_item_movies['age'] = user_item_movies['age'].map(age)


user_item_movies.drop('zip_code',axis=1,inplace=True)
user_item_geners.drop('zip_code',axis=1,inplace=True)


user_item_geners.rename(columns = {'user_id' : 'UserId', 'movie_id':'ItemId', 'rating': 'Rating'},inplace=True)
user_item_movies.rename(columns = {'user_id' : 'UserId'},inplace=True)


user_item_geners = user_item_geners[['ItemId', 'UserId','release_year', 'gender', 'age', 'occupation',
       'Rating', 'hour', 'avg_ratings', 'other', 'action', 'adventure',
       'animation', 'children', 'comedy', 'crime', 'documentary', 'drama',
       'fantasy', 'film-noir', 'horror', 'musical', 'mystery', 'romance',
       'sci-fi', 'thriller', 'western']]


display(user_item_geners.head(3))
display(user_item_movies.head(3))


user_movie_ratings =


def kde_boxplot(data, x, font_size, target=None):
    plt.figure(figsize = (20,7))
    
    plt.subplot(1,2,1)
    sns.kdeplot(data = data,
                x = x,
                hue = target
               )
    if target != None:
        plt.title(label = "Distribution of "+x+" for different "+target,
                 y = -0.2,
                 fontsize = font_size,
                 fontweight = 'bold')
    else:
        plt.title(label = "Distribution of "+x, 
                 y = -0.2,
                 fontsize = font_size,
                 fontweight = 'bold')
    plt.grid()
    
    plt.subplot(1,2,2)
    sns.boxplot(data=data,
               x = target,
               y = x)

    if target != None:
        plt.title(label = "Boxplot showing distribution of "+x+" for different "+target,
                 y = -0.2,
                 fontsize = font_size,
                 fontweight = 'bold')
        if df_grouped['City'].nunique() > 5:
            plt.xticks(rotation = 75)
    else:
        plt.title(label = "Boxplot showing distribution of "+x,
                 y = -0.2,
                 fontsize = font_size,
                 fontweight = 'bold')
    plt.grid(which='both')
    plt.show()


plt.figure(figsize = (12,8))
plt.subplot(1,2,1)
sns.barplot(data=user_ratings, x = 'age', y = 'rating',estimator=np.mean)
plt.xticks(rotation=90)
plt.title('Mean rating by age')

plt.subplot(1,2,2)
sns.boxplot(data=user_ratings, x = 'age',  y= 'rating')
plt.xticks(rotation=90)
plt.title('Age-wise distribution of rating')
plt.show()

plt.figure(figsize = (12,8))
plt.subplot(1,2,1)
sns.barplot(data=user_ratings, x = 'age', y = 'hour',estimator=np.mean)
plt.xticks(rotation=90)
plt.title('Mean rating by age')

plt.subplot(1,2,2)
sns.boxplot(data=user_ratings, x = 'age',  y= 'hour')
plt.xticks(rotation=90)
plt.title('Age-wise distribution of rating')
plt.show()

sns.kdeplot(data=user_ratings, x = 'hour',hue='age')
plt.title('Distribution of day hour of watch time')
plt.legend(loc= 'upper left')
plt.show()

sns.kdeplot(data=user_ratings, x = 'hour',hue='occupation',legend=False)
plt.title('Distribution of day hour of watch time by occupation')
plt.show()

WARNING:matplotlib.legend:No handles with labels found to put in legend.


eda_df = user_ratings.merge(movies_default, on = 'movie_id')
eda_df.head(3)


best_combo_geners = eda_df.groupby('geners')['rating'].mean().sort_values(ascending=False).head(20)
sns.barplot(x = best_combo_geners.index, y = best_combo_geners.values)
plt.title('Best rated combination of geners')
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize = (25,8))
sns.barplot(x = eda_df['release_year'].value_counts().index, y = eda_df['release_year'].value_counts().values)
plt.title('Number of movies release by year')
plt.xticks(rotation=90)
plt.show()


from sklearn.preprocessing import StandardScaler


ss1 = StandardScaler()
ss2 = StandardScaler()

user_item_movies.loc[:,'gender':] = ss1.fit_transform(user_item_movies.loc[:,'gender':])
user_item_geners.loc[:,'release_year': ] = ss2.fit_transform(user_item_geners.loc[:,'release_year':])

/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.
  warnings.warn(
/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.
  warnings.warn(


def pearson_rec(data,k,movie_id):
    temp = data.T.corr()
    mov = temp.loc[:, movie_id].sort_values(ascending=False)[:k].index
    display(movies_default.loc[movies_default.movie_id.isin(mov)].reset_index(drop=True))


pearson_rec(geners, 10, 6)


cos_similarity_item = pd.DataFrame(data = metrics.pairwise.cosine_similarity(geners),columns = geners.index, index  =geners.index )
display(cos_similarity_item)


cos_similarity_users = pd.DataFrame(data = metrics.pairwise.cosine_similarity(user_item_movies.iloc[:,1:]), index = user_item_movies['UserId'], columns = user_item_movies['UserId'])
display(cos_similarity_users)


def cosine_recommendations(geners, k, entitiy):
    cos = metrics.pairwise.cosine_similarity(geners)
    cos = pd.DataFrame(data=cos, columns = geners.index, index = geners.index)
    rec = (cos.loc[3].sort_values(ascending=False)).index[:k]
    rec = movies_default[movies_default.movie_id.isin(rec)][['title','movie_id','geners']]
    display(rec[['movie_id','title','geners']].reset_index(drop=True))

cosine_recommendations(geners,10,5)


dist = euclidian_distance(user_item_movies.loc[5 ,1:], user_item_movies.iloc[:,1:])
dist.index = dist.index + 1
u = dist.sort_values()[1:5].index
u_mov = ratings[ratings.user_id == 5]['movie_id']
rec_mov_id = (ratings[(ratings['user_id'].isin(u)) & (~ratings['movie_id'].isin(u_mov))].sort_values(by='rating',ascending=False))['movie_id'].values[:5]
display(movies_default[movies_default.movie_id.isin(rec_mov_id)])


def euclidian_distance(a,b):
  return (np.sum((a-b)**2,axis=1))**0.5

def NN_rec(user_id, num_mov):
  dist = euclidian_distance(user_item_movies.loc[user_id,1:], user_item_movies.iloc[:,1:])
  dist.index = dist.index + 1
  u = dist.sort_values()[1:5].index
  u_mov = ratings[ratings.user_id == user_id]['movie_id']
  rec_mov_id = (ratings[(~ratings['user_id'].isin(u)) & (~ratings['movie_id'].isin(u_mov))].sort_values(by='rating',ascending=False))['movie_id'].values[:num_mov]
  display(movies_default[movies_default.movie_id.isin(rec_mov_id)])


NN_rec(5,5)


eda_df[eda_df.user_id == 5]


pip install cmfrec

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting cmfrec
  Downloading cmfrec-3.5.0.post1.tar.gz (267 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 267.6/267.6 KB 6.5 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting findblas
  Using cached findblas-0.1.20-py3-none-any.whl
Requirement already satisfied: cython in /usr/local/lib/python3.8/dist-packages (from cmfrec) (0.29.33)
Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from cmfrec) (1.7.3)
Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.8/dist-packages (from cmfrec) (1.3.5)
Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from cmfrec) (1.21.6)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.25.0->cmfrec) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.25.0->cmfrec) (2022.7)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25.0->cmfrec) (1.15.0)
Building wheels for collected packages: cmfrec
  Building wheel for cmfrec (pyproject.toml) ... done
  Created wheel for cmfrec: filename=cmfrec-3.5.0.post1-cp38-cp38-linux_x86_64.whl size=6082762 sha256=312a1890a55d6841a18d00641c7ad4771d2cdf6f04c3f1e522616e03abfb2580
  Stored in directory: /root/.cache/pip/wheels/08/6b/25/1ebcd3aad4d02fb6f68d9b045cf9bb019d0d94c261eb8c6758
Successfully built cmfrec
Installing collected packages: findblas, cmfrec
Successfully installed cmfrec-3.5.0.post1 findblas-0.1.20


from cmfrec import CMF


model = CMF(k=7, lambda_=0.1, user_bias=False, item_bias=False, verbose=False)
model.fit(user_item_geners)

Collective matrix factorization model
(explicit-feedback variant)


print(f"A has {model.A_.shape[0]} row and {model.A_.shape[1]} columns")
print(f"B has {model.B_.shape[0]} row and {model.B_.shape[1]} columns")

A has 6040 row and 7 columns
B has 3682 row and 7 columns


top_items = model.topN(user=5, n=10)
movies_default.loc[movies_default.movie_id.isin(top_items)]


from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import mean_absolute_percentage_error as mape
rm__ = np.dot(model.A_, model.B_.T) + model.glob_mean_
print(f"RMSE for the model is {(mse(user_item.values[user_item > 0], rm__[user_item > 0])**0.5).round(2)}")
print(f"MAPE of the model is {(mape(user_item.values[user_item > 0], rm__[user_item > 0])).round(2)}")

RMSE for the model is 3.88
MAPE of the model is 1.08


user_item.values[user_item > 0].shape

(996144,)


rm__[user_item > 0].shape

(996144,)


eda_df['age'].value_counts().index[0]

'25-34'


eda_df['occupation'].value_counts().index[0]

'college/grad student'


eda_df['gender'].value_counts().index[0]

'M'


eda_df['release_year'].min()

1919.0


eda_df['decade'] = pd.cut(eda_df['release_year'], [1919, 1950, 1960, 1970, 1980, 1990, 2000, 2010], labels = ['before 50s', '50s', '60s', '70s', '80s', '90s', '2000+'])
eda_df['decade'].value_counts().index[0]

'90s'


(eda_df.groupby('title').agg({'rating':"count"}).reset_index().sort_values(by = 'rating', ascending=False)).iloc[0]

title     American Beauty (1999)
rating                      3428
Name: 127, dtype: object


movies[movies['title'].str.contains('Liar')]


cosine_recommendations(geners,10,1485)


pearson_rec(geners, 10, 1484)


print(f"RMSE for the model is {(mse(user_item.values[user_item > 0], rm__[user_item > 0])**0.5).round(2)}")
print(f"MAPE of the model is {(mape(user_item.values[user_item > 0], rm__[user_item > 0])).round(2)}")


from scipy.sparse import coo_matrix
dense_matrix = np.array([[1, 0], [3, 7]])
sparse_matrix = coo_matrix(dense_matrix)
print(sparse_matrix)

RMSE for the model is 3.88
MAPE of the model is 1.08
  (0, 0)	1
  (1, 0)	3
  (1, 1)	7

	Movie ID::Title::Genres	Unnamed: 1	Unnamed: 2
0	1::Toy Story (1995)::Animation\|Children's\|Comedy	NaN	NaN
1	2::Jumanji (1995)::Adventure\|Children's\|Fantasy	NaN	NaN
2	3::Grumpier Old Men (1995)::Comedy\|Romance	NaN	NaN

	UserID::MovieID::Rating::Timestamp
0	1::1193::5::978300760
1	1::661::3::978302109
2	1::914::3::978301968

	UserID::Gender::Age::Occupation::Zip-code
0	1::F::1::10::48067
1	2::M::56::16::70072
2	3::M::25::15::55117

	movie_id	title	geners	release_year
0	1	Toy Story (1995)	Animation\|Children's\|Comedy	1995.0
1	2	Jumanji (1995)	Adventure\|Children's\|Fantasy	1995.0
2	3	Grumpier Old Men (1995)	Comedy\|Romance	1995.0
3	4	Waiting to Exhale (1995)	Comedy\|Drama	1995.0
4	5	Father of the Bride Part II (1995)	Comedy	1995.0

	movie_id	release_year
count	3883.000000	3864.00000
mean	1986.049446	1986.09058
std	1146.778349	16.88945
min	1.000000	1919.00000
25%	982.500000	1982.00000
50%	2010.000000	1994.00000
75%	2980.500000	1997.00000
max	3952.000000	2000.00000

1. Define business problem statement and format the data to useable form.¶

1.1 Business Problem :¶

1.2 Formatting data file to bring them to workable form.¶

1.3 Insights :¶

2. Clean the data and performing EDA and feature engineering.¶

2.1 Clean the data¶

2.2 Performing Feature engineering¶

2.3 EDA¶

2.4 Standardizing¶

2.4 Insights :¶

3. Recommendation system based on Pearson Correlation¶

3.1 Recommendation using Pearson Correlation¶

3.2 Insights¶

4. Recommendation system using Cosine Similarity¶

4.1 User user and item item cosine similarity matrix¶

4.2 Recommendations using Cosine similarity¶

4.3 Insights¶

5. Recommendation using Nearest Neighbour¶

5.1 Recommendaiton using nearest neighbour :¶

5.2 Insights¶

6. Recommendation system based on matrix factorization¶

7. Final Insights and Recommendations¶

7.1 Insights¶

7.2 Recommendations¶

8.Questionnaire:¶

	user_id	gender	age	occupation	zip_code
0	1	F	Under 18	K-12 student	48067
1	2	M	56+	self-employed	70072
2	3	M	25-34	scientist	55117
3	4	M	45-49	executive/managerial	02460
4	5	M	25-34	writer	55455

	user_id
count	6040.000000
mean	3020.500000
std	1743.742145
min	1.000000
25%	1510.750000
50%	3020.500000
75%	4530.250000
max	6040.000000

	user_id	movie_id	rating	hour	avg_ratings
0	1	1193	5	22	4.19
1	1	661	3	22	4.19
2	1	914	3	22	4.19
3	1	3408	4	22	4.19
4	1	2355	5	23	4.19

	user_id	movie_id	rating	hour	avg_ratings
count	1.000209e+06	1.000209e+06	1.000209e+06	1.000209e+06	1.000209e+06
mean	3.024512e+03	1.865540e+03	3.581564e+00	1.191620e+01	3.581581e+00
std	1.728413e+03	1.096041e+03	1.117102e+00	7.894465e+00	4.360299e-01
min	1.000000e+00	1.000000e+00	1.000000e+00	0.000000e+00	1.020000e+00
25%	1.506000e+03	1.030000e+03	3.000000e+00	4.000000e+00	3.320000e+00
50%	3.070000e+03	1.835000e+03	4.000000e+00	1.400000e+01	3.610000e+00
75%	4.476000e+03	2.770000e+03	4.000000e+00	1.900000e+01	3.880000e+00
max	6.040000e+03	3.952000e+03	5.000000e+00	2.300000e+01	4.960000e+00

	movie_id	title	geners	release_year
0	1	Toy Story (1995)	animation\|children\|comedy	1995.0
1	2	Jumanji (1995)	adventure\|children\|fantasy	1995.0
2	3	Grumpier Old Men (1995)	comedy\|romance	1995.0
3	4	Waiting to Exhale (1995)	comedy\|drama	1995.0
4	5	Father of the Bride Part II (1995)	comedy	1995.0

movie_id	title	geners	release_year
1	Toy Story (1995)	animation	1995.0
1	Toy Story (1995)	children	1995.0
1	Toy Story (1995)	comedy	1995.0

	movie_id	title	geners	release_year
0	6	Heat (1995)	action\|crime\|thriller	1995.0
1	170	Hackers (1995)	action\|crime\|thriller	1995.0
2	464	Hard Target (1993)	action\|adventure\|crime\|thriller	1993.0
3	1953	French Connection, The (1971)	action\|crime\|drama\|thriller	1971.0
4	2126	Snake Eyes (1998)	action\|crime\|mystery\|thriller	1998.0
5	2278	Ronin (1998)	action\|crime\|thriller	1998.0
6	2540	Corruptor, The (1999)	action\|crime\|drama\|thriller	1999.0
7	2956	Someone to Watch Over Me (1987)	action\|crime\|thriller	1987.0
8	3763	F/X (1986)	action\|crime\|thriller	1986.0
9	3764	F/X 2 (1992)	action\|crime\|thriller	1992.0

	movie_id	title	geners
0	276	Milk Money (1994)	comedy\|romance
1	287	Nina Takes a Lover (1994)	comedy\|romance
2	289	Only You (1994)	comedy\|romance
3	294	Perez Family, The (1995)	comedy\|romance
4	295	Pyromaniac's Love Story, A (1995)	comedy\|romance
5	752	Vermont Is For Lovers (1992)	comedy\|romance
6	1541	Addicted to Love (1997)	comedy\|romance
7	1854	Kissing a Fool (1998)	comedy\|romance
8	2339	I'll Be Home For Christmas (1998)	comedy\|romance
9	3501	Murphy's Romance (1985)	comedy\|romance

	movie_id	title	geners	release_year
108	110	Braveheart (1995)	action\|drama\|western	1995.0
551	555	True Romance (1993)	action\|crime\|romance	1993.0
1176	1193	One Flew Over the Cuckoo's Nest (1975)	drama	1975.0
1373	1394	Raising Arizona (1987)	comedy	1987.0
2302	2371	Fletch (1985)	comedy	1985.0

	user_id	gender	age	occupation	zip_code	movie_id	rating	hour	avg_ratings	title	geners	release_year	decade
2887	5	M	25-34	writer	55455	3408	3	5	3.15	Erin Brockovich (2000)	drama	2000.0	90s
4203	5	M	25-34	writer	55455	2355	5	5	3.15	Bug's Life, A (1998)	animation\|children\|comedy	1998.0	90s
11042	5	M	25-34	writer	55455	919	4	5	3.15	Wizard of Oz, The (1939)	adventure\|children\|drama\|musical	1939.0	before 50s
19505	5	M	25-34	writer	55455	3105	2	7	3.15	Awakenings (1990)	drama	1990.0	80s
31153	5	M	25-34	writer	55455	1721	1	6	3.15	Titanic (1997)	drama\|romance	1997.0	90s
...	...	...	...	...	...	...	...	...	...	...	...	...	...
319592	5	M	25-34	writer	55455	3786	3	5	3.15	But I'm a Cheerleader (1999)	comedy	1999.0	90s
319776	5	M	25-34	writer	55455	34	4	6	3.15	Babe (1995)	children\|comedy\|drama	1995.0	90s
321527	5	M	25-34	writer	55455	357	2	6	3.15	Four Weddings and a Funeral (1994)	comedy\|romance	1994.0	90s
322760	5	M	25-34	writer	55455	36	3	6	3.15	Dead Man Walking (1995)	drama	1995.0	90s
323688	5	M	25-34	writer	55455	714	4	6	3.15	Dead Man (1995)	western	1995.0	90s

geners		A	Acti	Action	Adv	Advent	Adventu	Adventur	Adventure	Animati	Animation	Chi	Chil	Childr	Childre	Children	Children'	Children's	Com	Come	Comed	Comedy	Crime	D	Docu	Documen	Document	Documenta	Documentary	Dr	Dram	Drama	F	Fant	Fantas	Fantasy	Film-Noir	Horr	Horro	Horror	Music	Musical	Mystery	R	Ro	Rom	Roma	Roman	Romance	S	Sci	Sci-	Sci-F	Sci-Fi	Th	Thri	Thrille	Thriller	Wa	War	We	Wester	Western
movie_id
1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0

geners		action	adventure	animation	children	comedy	crime	documentary	drama	fantasy	film-noir	horror	musical	mystery	romance	sci-fi	thriller	western
movie_id
1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0
2	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0
3	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0

	UserId	gender	age	occupation	1	21	48	95	104	110	150	163	165	235	260	265	292	318	349	356	368	380	434	442	457	459	480	498	515	527	531	552	588	589	590	593	594	595	608	647	648	653	661	720	733	736	745	780	783	902	914	919	920	938	982	1022	1028	1029	1035	1049	1079	1084	1090	1096	1097	1103	1124	1136	1188	1193	1196	1197	1198	1207	1210	1213	1217	1225	1244	1245	1246	1247	1253	1259	1261	1265	1266	1270	1287	1291	1293	1304	1357	1370	1372	1378	1379	1385	1394	1408	1431	1442	1527	1537	1544	1545	1552	1566	1580	1597	1610	1615	1641	1687	1690	1721	1784	1792	1801	1834	1836	1873	1907	1917	1945	1953	1954	1955	1957	1961	1962	1968	2002	2006	2018	2028	2067	2081	2115	2126	2167	2194	2236	2268	2278	2294	2312	2321	2340	2353	2355	2359	2396	2398	2427	2470	2490	2501	2571	2617	2628	2687	2692	2717	2728	2735	2762	2791	2797	2804	2852	2858	2871	2881	2916	2918	2943	2997	3030	3035	3068	3071	3095	3105	3107	3108	3114	3147	3168	3186	3255	3256	3257	3334	3408	3418	3421	3451	3468	3471	3534	3552	3578	3619	3654	3671	3678	3699	3735	3809	3868	3893
0	1	0	18	1	5.0	0.0	5.0	0.0	0.0	0.0	5.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	4.0	0.0	4.0	0.0	0.0	0.0	4.0	5.0	4.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	3.0	0.0	4.0	0.0	3.0	4.0	0.0	4.0	0.0	5.0	5.0	5.0	5.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	5.0	0.0	3.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	5.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	4.0	0.0	0.0	0.0	4.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	3.0	3.0	0.0	5.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	4.0	0.0	0.0	0.0	4.0	4.0	4.0	5.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	0.0	4.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	2	1	56	1	0.0	1.0	0.0	2.0	0.0	5.0	0.0	4.0	3.0	3.0	0.0	4.0	3.0	5.0	4.0	5.0	4.0	5.0	2.0	3.0	4.0	3.0	5.0	3.0	5.0	0.0	0.0	0.0	0.0	4.0	5.0	5.0	0.0	0.0	0.0	3.0	4.0	0.0	0.0	0.0	0.0	4.0	0.0	3.0	0.0	2.0	0.0	0.0	5.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	2.0	4.0	0.0	3.0	5.0	0.0	4.0	5.0	5.0	0.0	4.0	4.0	4.0	2.0	3.0	5.0	3.0	2.0	5.0	5.0	3.0	5.0	0.0	3.0	0.0	0.0	0.0	0.0	5.0	0.0	5.0	5.0	3.0	0.0	0.0	3.0	0.0	3.0	0.0	4.0	4.0	4.0	4.0	0.0	3.0	0.0	0.0	3.0	5.0	0.0	0.0	3.0	3.0	0.0	5.0	3.0	3.0	4.0	0.0	4.0	0.0	3.0	5.0	4.0	5.0	4.0	5.0	0.0	5.0	2.0	5.0	3.0	0.0	4.0	5.0	0.0	0.0	3.0	0.0	4.0	5.0	5.0	3.0	0.0	3.0	3.0	0.0	4.0	0.0	3.0	4.0	0.0	2.0	0.0	3.0	5.0	4.0	0.0	3.0	0.0	0.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	3.0	4.0	0.0	3.0	3.0	0.0	4.0	0.0	4.0	4.0	4.0	4.0	4.0	4.0	2.0	3.0	0.0	5.0	0.0	0.0	4.0	2.0	3.0	4.0	0.0	4.0	0.0	4.0	5.0	5.0	0.0	0.0	5.0	0.0	3.0	0.0	3.0	2.0	3.0	3.0	0.0	1.0
2	3	1	34	0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	4.0	3.0	0.0	0.0	0.0	0.0	3.0	4.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	0.0	4.0	5.0	5.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	1.0	2.0	5.0	3.0	0.0	4.0	0.0	5.0	0.0	0.0	0.0	5.0	4.0	0.0	4.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	5.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	4.0	0.0	4.0	0.0	0.0	0.0	4.0	4.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	3.0	5.0	0.0	2.0	0.0	5.0	0.0	0.0	0.0	0.0	3.0	0.0

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movie_id
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	movie_id	title	geners	release_year
330	334	Vanya on 42nd Street (1994)	drama	1994.0
664	670	World of Apu, The (Apur Sansar) (1959)	drama	1959.0
1240	1260	M (1931)	crime\|film-noir\|thriller	1931.0
1241	1261	Evil Dead II (Dead By Dawn) (1987)	action\|adventure\|comedy\|horror	1987.0
1327	1348	Nosferatu (Nosferatu, eine Symphonie des Graue...	horror	1922.0
1535	1575	Gabbeh (1996)	drama	1996.0
2443	2512	Ballad of Narayama, The (Narayama Bushiko) (1982)	drama	1982.0
2770	2839	West Beirut (West Beyrouth) (1998)	drama	1998.0
2953	3022	General, The (1927)	comedy	1927.0
3065	3134	Grand Illusion (Grande illusion, La) (1937)	drama\|western	1937.0

	movie_id	title	geners	release_year
1455	1485	Liar Liar (1997)	comedy	1997.0
2813	2882	Jakob the Liar (1999)	drama	1999.0

	movie_id	title	geners	release_year
0	652	301, 302 (1995)	mystery	1995.0
1	950	Thin Man, The (1934)	mystery	1934.0
2	977	Moonlight Murder (1936)	mystery	1936.0
3	1065	Woman in Question, The (1950)	mystery	1950.0
4	1464	Lost Highway (1997)	mystery	1997.0
5	1484	Daytrippers, The (1996)	mystery	1996.0
6	2467	Name of the Rose, The (1986)	mystery	1986.0
7	2583	Cookie's Fortune (1999)	mystery	1999.0
8	2945	Mike's Murder (1984)	mystery	1984.0
9	3149	Diamonds (1999)	mystery	1999.0